






'''simple docstring'''





import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


_UpperCamelCase						:						Tuple  =					threading.Lock()
_UpperCamelCase						:						Optional[logging.Handler]  =					None

_UpperCamelCase						:						Optional[int]  =					{
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

_UpperCamelCase						:						Optional[int]  =					logging.WARNING

_UpperCamelCase						:						Optional[int]  =					True
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       List[str]          =       os.getenv('''TRANSFORMERS_VERBOSITY'''			,       A		)
			if env_level_str:
						if env_level_str in log_levels:
									return log_levels[env_level_str]
						else:
									logging.getLogger().warning(
									    F"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
									    F"has to be one of: { ', '.join(log_levels.keys()		) }"		)
			return _default_log_level
def __UpperCAmelCase   (					)    ->   str:
			return __name__.split('''.'''		)[0]
def __UpperCAmelCase   (					)    ->   logging.Logger:
			return logging.getLogger(_get_library_name()		)
def __UpperCAmelCase   (					)    ->   None:
			global _default_handler

			with _lock:
						if _default_handler:
									# This library has already configured the library root logger.
									return
						UpperCAmelCase_      :       Union[str, Any]          =       logging.StreamHandler()  # Set sys.stderr as stream.
						UpperCAmelCase_      :       str          =       sys.stderr.flush

						# Apply our default configuration to the library root logger.
						UpperCAmelCase_      :       Dict          =       _get_library_root_logger()
						library_root_logger.addHandler(_default_handler		)
						library_root_logger.setLevel(_get_default_logging_level()		)
						UpperCAmelCase_      :       Any          =       False
def __UpperCAmelCase   (					)    ->   None:
			global _default_handler

			with _lock:
						if not _default_handler:
									return

						UpperCAmelCase_      :       Optional[Any]          =       _get_library_root_logger()
						library_root_logger.removeHandler(_default_handler		)
						library_root_logger.setLevel(logging.NOTSET		)
						UpperCAmelCase_      :       Optional[int]          =       None
def __UpperCAmelCase   (					)    ->   List[Any]:
			return log_levels
def __UpperCAmelCase   (					A					:			Optional[str] = None		)    ->   logging.Logger:

			if name is None:
						UpperCAmelCase_      :       Union[str, Any]          =       _get_library_name()

			_configure_library_root_logger()
			return logging.getLogger(A		)
def __UpperCAmelCase   (					)    ->   int:
			_configure_library_root_logger()
			return _get_library_root_logger().getEffectiveLevel()
def __UpperCAmelCase   (					A					:			int		)    ->   None:
			_configure_library_root_logger()
			_get_library_root_logger().setLevel(A		)
def __UpperCAmelCase   (					)    ->   str:
			return set_verbosity(A		)
def __UpperCAmelCase   (					)    ->   str:
			return set_verbosity(A		)
def __UpperCAmelCase   (					)    ->   Optional[Any]:
			return set_verbosity(A		)
def __UpperCAmelCase   (					)    ->   Dict:
			return set_verbosity(A		)
def __UpperCAmelCase   (					)    ->   None:
			_configure_library_root_logger()

			assert _default_handler is not None
			_get_library_root_logger().removeHandler(_default_handler		)
def __UpperCAmelCase   (					)    ->   None:
			_configure_library_root_logger()

			assert _default_handler is not None
			_get_library_root_logger().addHandler(_default_handler		)
def __UpperCAmelCase   (					A					:			logging.Handler		)    ->   None:
			_configure_library_root_logger()

			assert handler is not None
			_get_library_root_logger().addHandler(A		)
def __UpperCAmelCase   (					A					:			logging.Handler		)    ->   None:
			_configure_library_root_logger()

			assert handler is not None and handler not in _get_library_root_logger().handlers
			_get_library_root_logger().removeHandler(A		)
def __UpperCAmelCase   (					)    ->   None:
			_configure_library_root_logger()
			UpperCAmelCase_      :       Any          =       False
def __UpperCAmelCase   (					)    ->   None:
			_configure_library_root_logger()
			UpperCAmelCase_      :       Dict          =       True
def __UpperCAmelCase   (					)    ->   None:
			UpperCAmelCase_      :       Optional[int]          =       _get_library_root_logger().handlers

			for handler in handlers:
						UpperCAmelCase_      :       Dict          =       logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''		)
						handler.setFormatter(A		)
def __UpperCAmelCase   (					)    ->   None:
			UpperCAmelCase_      :       List[Any]          =       _get_library_root_logger().handlers

			for handler in handlers:
						handler.setFormatter(A		)
def __UpperCAmelCase   (					self					:			Tuple			,       *A					:			Any			,       **A					:			Union[str, Any]		)    ->   Tuple:
			UpperCAmelCase_      :       Dict          =       os.getenv('''TRANSFORMERS_NO_ADVISORY_WARNINGS'''			,       A		)
			if no_advisory_warnings:
						return
			self.warning(*A			,       **A		)


_UpperCamelCase						:						str  =					warning_advice
@functools.lru_cache(A		)
def __UpperCAmelCase   (					self					:			Dict			,       *A					:			str			,       **A					:			List[str]		)    ->   Optional[int]:
			self.warning(*A			,       **A		)


_UpperCamelCase						:						List[str]  =					warning_once


class  snake_case__      :




					def __init__(  self  :		Optional[Any]				, *_A  :		Optional[int]				, **_A  :		List[str]      )    ->							Tuple:  # pylint: disable=unused-argument
								UpperCAmelCase_      :       Optional[Any]          =       args[0] if args else None




					def __iter__(  self  :		Union[str, Any]      )    ->							List[str]:
								return iter(self._iterator      )




					def __getattr__(  self  :		Tuple				, _A  :		List[Any]      )    ->							Any:

								def empty_fn(*_A  :		List[str]				, **_A  :		Dict      ):  # pylint: disable=unused-argument
											return

								return empty_fn




					def __enter__(  self  :		Optional[int]      )    ->							Tuple:
								return self





					def __exit__(  self  :		Optional[int]				, _A  :		Optional[Any]				, _A  :		int				, _A  :		Optional[Any]      )    ->							List[Any]:
								return






class  snake_case__      :




					def __call__(  self  :		Any				, *_A  :		Dict				, **_A  :		Union[str, Any]      )    ->							Optional[int]:
								if _tqdm_active:
											return tqdm_lib.tqdm(*_A				, **_A      )
								else:
											return EmptyTqdm(*_A				, **_A      )




					def 		A      (  self  :		Any				, *_A  :		List[Any]				, **_A  :		Any      )    ->							Dict:
								UpperCAmelCase_      :       Union[str, Any]          =       None
								if _tqdm_active:
											return tqdm_lib.tqdm.set_lock(*_A				, **_A      )





					def 		A      (  self  :		Optional[int]      )    ->							Optional[int]:
								if _tqdm_active:
											return tqdm_lib.tqdm.get_lock()






_UpperCamelCase						:						Union[str, Any]  =					_tqdm_cls()
def __UpperCAmelCase   (					)    ->   bool:
			global _tqdm_active
			return bool(_tqdm_active		)
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			global _tqdm_active
			UpperCAmelCase_      :       int          =       True
			hf_hub_utils.enable_progress_bars()







def __UpperCAmelCase   (					)    ->   Any:
			global _tqdm_active
			UpperCAmelCase_      :       Optional[int]          =       False
			hf_hub_utils.disable_progress_bars()










'''simple docstring'''





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						int  =					32
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[Any]:
			return int(x / 2**2_0		)


class  snake_case__      :




					def __enter__(  self  :		List[Any]      )    ->							str:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								UpperCAmelCase_      :       Dict          =       torch.cuda.memory_allocated()
								return self





					def __exit__(  self  :		Optional[Any]				, *_A  :		Any      )    ->							Any:
								gc.collect()
								torch.cuda.empty_cache()
								UpperCAmelCase_      :       Optional[int]          =       torch.cuda.memory_allocated()
								UpperCAmelCase_      :       int          =       torch.cuda.max_memory_allocated()
								UpperCAmelCase_      :       int          =       bamb(self.end - self.begin      )
								UpperCAmelCase_      :       List[str]          =       bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6			,       A					:			str = "bert-base-cased"			,       A					:			int = 3_2_0			,       A					:			int = 1_6_0			,       )    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(A		)
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}		)

			def tokenize_function(A					:			int		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       Optional[int]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_      :       Union[str, Any]          =       datasets.map(
			    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=A		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       Optional[int]          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			List[Any]		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''		)
						return tokenizer.pad(A			,       padding='''longest'''			,       return_tensors='''pt'''		)

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Optional[Any]          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       List[Any]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   Dict:
			# Initialize accelerator
			UpperCAmelCase_      :       Dict          =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       Optional[int]          =       config['''lr''']
			UpperCAmelCase_      :       List[Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Any          =       int(config['''seed''']		)
			UpperCAmelCase_      :       List[str]          =       int(config['''batch_size''']		)
			UpperCAmelCase_      :       Tuple          =       args.model_name_or_path

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       get_dataloaders(A			,       A			,       A			,       args.n_train			,       args.n_val		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Optional[Any]          =       AutoModelForSequenceClassification.from_pretrained(A			,       return_dict=A		)

			# Instantiate optimizer
			UpperCAmelCase_      :       str          =       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_      :       Optional[Any]          =       optimizer_cls(params=model.parameters()			,       lr=A		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_      :       Tuple          =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase_      :       Tuple          =       1
			UpperCAmelCase_      :       List[Any]          =       (len(A		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_      :       List[str]          =       get_linear_schedule_with_warmup(
						    optimizer=A			,       num_warmup_steps=0			,       num_training_steps=A			,       )
			else:
						UpperCAmelCase_      :       Optional[Any]          =       DummyScheduler(A			,       total_num_steps=A			,       warmup_num_steps=0		)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_      :       Optional[Any]          =       0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_      :       List[str]          =       0

			# Now we train the model
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for epoch in range(A			,       A		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(A		):
												UpperCAmelCase_      :       List[Any]          =       model(**A		)
												UpperCAmelCase_      :       List[str]          =       outputs.loss
												UpperCAmelCase_      :       Union[str, Any]          =       loss / gradient_accumulation_steps
												accelerator.backward(A		)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin		)		)		)
						accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used		)		)
						accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked		)		)
						accelerator.print(
						    '''Total Peak Memory consumed during the train (max): {}'''.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
						UpperCAmelCase_      :       List[str]          =       tracemalloc.peaked + bamb(tracemalloc.begin		)
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''		)			,       '''w'''		) as f:
									json.dump(A			,       A		)
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Dict          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       type=A			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=A			,       )
			parser.add_argument(
			    '''--output_dir'''			,       type=A			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
			parser.add_argument(
			    '''--peak_memory_upper_bound'''			,       type=A			,       default=A			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
			parser.add_argument(
			    '''--n_train'''			,       type=A			,       default=3_2_0			,       help='''Number of training examples to use.'''			,       )
			parser.add_argument(
			    '''--n_val'''			,       type=A			,       default=1_6_0			,       help='''Number of validation examples to use.'''			,       )
			parser.add_argument(
			    '''--num_epochs'''			,       type=A			,       default=1			,       help='''Number of train epochs.'''			,       )
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[Any]          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()


